Autonomous robotic systems (ARS) (e.g. drones, self driving cars, etc.) require an awareness of their surroundings in order to plan their paths, avoid obstacles, and generally carry out their mission. In a specific example, an interceptor drone needs a spatial and temporal awareness to effectively detect and track an object. Such awareness is critical in providing the drone the ability to avoid other flying objects, re-detect lost target and contribute to optimal path planning. Spatial and temporal awareness requires extensive computation of sensor data representing an ARS's surroundings. Related art machine vision algorithms are typically optimized for high performance and power intensive hardware (e.g., data centers, computers on autonomous cars, etc.). However, light weight and high-speed related art ARSs have limited computing hardware and power supplies and thus cannot devote a lot of resources for vision processing. Thus there is an unmet need in the related art for systems and methods for optimization of the limited power and computing resources available onboard an ARS.
Autonomous robotic systems (ARS) tasked with detection and tracking of objects can often suffer from the lack of tracking if the target moves out of a sensor's field of view. In one example, visual detection and tracking of moving targets suffers from target loss when the target leaves a camera's viewport due to the relative movement between the target and the camera. In such situations recovery from a full loss of target from visual data alone is very unlikely if not impossible without a wide area sweep of the ARS's surroundings. This is costly both in time and computational resources and thus very undesirable. Thus there is a need for systems and methods that provide recovery of a target that are both speedy and not computationally intensive.